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UFCFE3-15-1 Introductory Artificial Intelligence for Robotics

Question

Answered

Question:

This assignment assesses the following module learning outcomes:

   Design and implement intelligent solutions using at least two different architectures.
   Design and implement a range of different search methods.

Working on this assignment will help you to:

·Understand how a search-based framework can be used to solve different problems;
·Gain experience of the practical issues around creating machine learning systems;
·Demonstrate that you can implement a simple Machine Learning algorithm.

The lectures you will have been introduced to various paradigms for representing models. In the tutorials you will have been introduced to an example C code framework for creating search-based solutions to problems.

The task is to use that framework to create a Machine Learning algorithm that can learn predictive Rule-Based models, using a greedy hill-climbing approach.

The system will present your code using a number of  datasets. Each has 150 examples, split 100:50 between a ‘training set’ and ‘test’ set. Each example contains integer values for 6 features, and a single integer giving the class label between 0 and 5.  Your code should:

·implement a train() function that  ‘learn’ a set of rules from the training set.  Some of the marks will be for implementing the right learning algorithm. For simplicity, your code should not generate solutions with more than 5 rules.

·implement a predictLabel() function that provide a predictions for a new case. Some of the marks will be for the effectiveness of your solution, i.e., its accuracy.  

Design and implement an effective rule-based architecture for learning predictive models from training sets    Solution will compute the difference D between the target and measured accuracy (in %) on test set.

Implement the specified search based algorithm for Machine Learning within the chosen architecture.    Marking system will automatically detect calls to functions provided within code framework.

Marking Criteria

This coursework is being marked by an automated system. You will be allowed six submission attempts and the highest mark will be taken.  

·Your code will be compiled and tested automatically on four data sets with different characteristics. At least one of these will be different for every student. The total mark awarded for a submission will be the average of the marks on the three datasets

·The datasets have been designed to have different ‘target’ accuracies. The marks for the effectiveness of your Machine Learning system will depend on how close you get to that target.

·The version of the supporting code framework used in the marking system will be instrumented to provide outputs about how it is being used.  This will be used to mark your implementation of the search algorithm. To emphasize the concept of search as a generic process, marks will be deducted for failure to use the framework.

UFCFE3-15-1 Introductory Artificial Intelligence for Robotics

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